The newly developed Huber mean provides a more stable and reliable way to compute averages for data lying on curved geometric spaces, or Riemannian manifolds. By combining the strengths of ...

Robust estimation and outlier detection play a critical role in modern data analysis, particularly when dealing with high-dimensional datasets. In such contexts, classical statistical methods often ...

Divergence estimators have emerged as quintessential tools in statistical inference, particularly in contexts where traditional likelihood‐based methods fail under model misspecification or data ...

Dr Max Welz introduces research aiming to make statistical analyses robust against so-called ‘contamination’ in rating data stemming from low-quality survey responses. Empirical research in the social ...

Scientists from Peking University conducts a systematic review of studies on integrating machine learning into statistical methods in disease prediction models. Researchers from Peking University have ...

Abstract: Assumptions play a pivotal role in the selection and efficacy of statistical models, as unmet assumptions can lead to flawed conclusions and impact decision-making. In both traditional ...

Kaitlyn Cook is a biostatistician working to develop robust statistical methods for infectious disease treatment and prevention trials. Her research draws on ideas from the missing data literature, ...

Chongzhi Di develops statistical methods for analyzing functional and longitudinal data in epidemiologic studies of physical activity and sedentary behavior, particularly using data from mobile-health ...

A new robust statistical method resists outliers, improving data reliability in AI, robotics, and medical imaging BUSAN, South Korea, Nov. 13, 2025 /PRNewswire/ -- In many modern sciences, data often ...